Multitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorRiverside-
Autor(es): dc.contributorU.S. Salinity Laboratory-
Autor(es): dc.contributorUniversity of California-
Autor(es): dc.creatorMinhoni, Renata Teixeira de Almeida [UNESP]-
Autor(es): dc.creatorScudiero, Elia-
Autor(es): dc.creatorZaccaria, Daniele-
Autor(es): dc.creatorSaad, João Carlos Cury [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:54:03Z-
Data de disponibilização: dc.date.available2022-02-22T00:54:03Z-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-06-25-
Data de envio: dc.date.issued2021-08-25-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.scitotenv.2021.147216-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/208671-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/208671-
Descrição: dc.descriptionSoil organic carbon (SOC) plays a crucial role for soil health. However, large datasets needed to accurately assess SOC at high resolution across scales are labor-intensive, time-consuming, and expensive. Ancillary geodata, including remote sensing spectral indices (RS-SIs) and topographic indicators (TIs), have been proposed as spatial covariates. Reported relationships between SOC and RS-SIs are erratic, possibly because single-date RS-SIs do not accurately capture SOC spatial variability due to transient confounding factors in the soil (e.g., moisture). However, multitemporal RS-SI data analysis may lead to noise reduction in SOC versus RS-SI relationships. This study aimed at: i) comparing single-date versus multitemporal RS-Sis derived from Sentinel-2 imagery for assessment of topsoil (0–0.2 m) SOC in two agricultural fields located in south-eastern Brazil; ii) comparing the performance of RS-SIs and TIs; iii) using adequate RS-SIs and TIs to compare sampling schemes defined on different collection grids; and iv) studying the temporal changes of SOC (0–0.2 m and 0.2–0.4 m). Results showed that: i) single-date RS-SIs were not reliable proxies for topsoil SOC at the study sites. For most of the tested RS-SIs, multitemporal data analysis produced accurate proxies for SOC; e.g., for the Normalized Difference Vegetation Index, the 4.5th multitemporal percentile predicted SOC with an R2 of 0.64; ii) The best TI was elevation (ranging from 643 to 684 m) with an R2 of 0.70; iii) The multitemporal SI and elevation maps indicated that the different sampling schemes were equally representative of the topsoil SOC's distribution across the entire area; and iv) From 2012 through 2019, topsoil SOC increased from 19.3 to 24.1 g kg−1. The ratio between SOC in the topsoil and subsoil (0.2–0.4 m) decreased from 1.7 to 1.1. Further testing of the proposed multitemporal RS-SI analysis is necessary to confirm its dependability for SOC assessment in Brazil and elsewhere.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionSão Paulo State University São Paulo State University (UNESP) School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780-
Descrição: dc.descriptionUniversity of California Riverside Department of Environmental Sciences, 900 University Ave.-
Descrição: dc.descriptionUnited States Department of Agriculture – Agricultural Research Service U.S. Salinity Laboratory, 450 West Big Springs Rd.-
Descrição: dc.descriptionDepartment of Land Air and Water Resources University of California-
Descrição: dc.descriptionSão Paulo State University São Paulo State University (UNESP) School of Agronomical Sciences, Campus Botucatu, Av. Universitária, 3780-
Descrição: dc.descriptionCNPq: 140676/2017-1-
Idioma: dc.languageen-
Relação: dc.relationScience of the Total Environment-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectCrop rotation-
Palavras-chave: dc.subjectReduced tillage-
Palavras-chave: dc.subjectRemote sensing-
Palavras-chave: dc.subjectSentinel-2-
Palavras-chave: dc.subjectSpectral indices-
Palavras-chave: dc.subjectSustainable agriculture-
Título: dc.titleMultitemporal satellite imagery analysis for soil organic carbon assessment in an agricultural farm in southeastern Brazil-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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